Evidence map›Paper›PMID 31906978›Full record

ArticleJournal of translational medicine2020

Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA.

Pei Chen, Shuo Li, Wenyuan Li, Jie Ren, Fengzhu Sun, Rui Liu, Xianghong Jasmine Zhou

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.6field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 17 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 2 countries.

Pei ChenSchool of Mathematics, South China University of Technology, Guangzhou, 510640, China.
Shuo LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, 90095, USA.
Wenyuan LiDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, 90095, USA.
Jie RenQuantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA.
Fengzhu SunQuantitative and Computational Biology Program, Department of Biological Sciences, University of Southern California, Los Angeles, CA, 90089, USA.
Rui LiuSchool of Mathematics, South China University of Technology, Guangzhou, 510640, China. scliurui@scut.edu.cn.ORCID 0000-0002-4547-8695
Xianghong Jasmine ZhouDepartment of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, 90095, USA. XJZhou@mednet.ucla.edu.
University of California, Los Angeles · USUniversity of Southern California · US

Funding

Novel Computation Methods for the Analysis of Cell-Free DNA Sequence DataR01CA246329 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DUBINETT, STEVEN M., LI, WENYUAN · 2019 to 2022
$2.2M
Informatics resources for liquid biopsy researchU01CA237711 · NCI · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI LI, WENYUAN · 2019 to 2021
$1.1M
NCI NIH HHS R01 CA246329NCI NIH HHS U01 CA237711
6 · The paper itself

Abstract

backgroundSepsis remains a major challenge in intensive care units, causing unacceptably high mortality rates due to the lack of rapid diagnostic tools with sufficient sensitivity. Therefore, there is an urgent need to replace time-consuming blood cultures with a new method. Ideally, such a method also provides comprehensive profiling of pathogenic bacteria to facilitate the treatment decision.

methodsWe developed a Random Forest with balanced subsampling to screen for pathogenic bacteria and diagnose sepsis based on cell-free DNA (cfDNA) sequencing data in a small blood sample. In addition, we constructed a bacterial co-occurrence network, based on a set of normal and sepsis samples, to infer unobserved bacteria.

resultsBased solely on cfDNA sequencing information from three independent datasets of sepsis, we distinguish sepsis from healthy samples with a satisfactory performance. This strategy also provides comprehensive bacteria profiling, permitting doctors to choose the best treatment strategy for a sepsis case.

conclusionsThe combination of sepsis identification and bacteria-inferring strategies is a success for noninvasive cfDNA-based diagnosis, which has the potential to greatly enhance efficiency in disease detection and provide a comprehensive understanding of pathogens. For comparison, where a culture-based analysis of pathogens takes up to 5 days and is effective for only a third to a half of patients, cfDNA sequencing can be completed in just 1 day and our method can identify the majority of pathogens in all patients.

Indexed as

Cell-Free Nucleic AcidsSepsisBacteriaDNA, BacterialHumansIntensive Care UnitsCell-Free Nucleic AcidsDNA, BacterialBacteremiaBacterial co-occurrence networkBacteria profilingCell-free DNA sequenceRapid diagnosisSepsis

Identifiers

PMID31906978
PMCPMC6943891
OpenAlexW3000293644

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.